Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging

doi:10.1007/s00330-018-5986-x

	Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
	Chen, Shuling 1; Feng, Shiting 2; Wei, Jingwei3,4,5 ; Liu, Fei3,4,5 ; Li, Bin 6; Li, Xin 7; Hou, Yang 8; Gu, Dongsheng3,4,5 ; Tang, Mimi 9; Xiao, Han 9; Jia, Yingmei 2; Peng, Sui 6,9; Tian, Jie 3,4,5; Kuang, Ming 1,10
发表期刊	EUROPEAN RADIOLOGY
ISSN	0938-7994
	2019-08-01
卷号	29 期号:8 页码:4177-4187
通讯作者	Tian, Jie(tian@ieee.org) ; Kuang, Ming(kuangminda@hotmail.com)
摘要	ObjectivesImmunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0-2 vs. 3-4) in HCC.Materials and methodsThe study included 207 (training cohort: n=150; validation cohort: n=57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model).ResultsThe combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855-0.953) vs. 0.823 (95% CI 0.747-0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0926 (95% CI 0884-0967) vs. 0904 (95% CI 0855-0953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement.ConclusionThe MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions.Key Points center dot Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma.center dot Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore.center dot We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.
关键词	Carcinoma Hepatocellular Gadolinium ethoxybenzyl DTPA Magnetic resonance imaging Immunotherapy
DOI	10.1007/s00330-018-5986-x
关键词[WOS]	TUMOR-INFILTRATING LYMPHOCYTES ; CD8(+) T-CELLS ; CARCINOMA ; FEATURES ; LEVEL ; CLASSIFICATION ; RECURRENCE ; EXPRESSION ; PATTERNS ; DENSITY
收录类别	SCI
语种	英语
资助项目	Guangzhou Science and Technology Program key projects[201803010057] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81771908] ; National Natural Science Foundation of China[81571750] ; Ministry of Science and Technology of China[2017YFA0205200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Key International Cooperation Projects of the Chinese Academy of Sciences[173211KYSB20160053] ; Guangzhou Science and Technology Program key projects[201803010057] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81771908] ; National Natural Science Foundation of China[81571750] ; Ministry of Science and Technology of China[2017YFA0205200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Key International Cooperation Projects of the Chinese Academy of Sciences[173211KYSB20160053]
项目资助者	Guangzhou Science and Technology Program key projects ; National Natural Science Foundation of China ; Ministry of Science and Technology of China ; Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission ; Key International Cooperation Projects of the Chinese Academy of Sciences
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000473737100025
出版者	SPRINGER
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：103[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/26880
专题	中国科学院分子影像重点实验室
通讯作者	Tian, Jie; Kuang, Ming
作者单位	1.Sun Yat Sen Univ, Affiliated Hosp 1, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, 58 Zhong Shan Rd 2, Guangzhou 510080, Guangdong, Peoples R China 2.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Radiol, 58 Zhong Shan Rd 2, Guangzhou 510080, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China 4.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Sun Yat Sen Univ, Affiliated Hosp 1, Clin Trial Unit, 58 Zhong Shan Rd 2, Guangzhou 510080, Guangdong, Peoples R China 7.GE HealthCare China, Shanghai 200000, Peoples R China 8.Jinan Univ, Dept Math, Guangzhou 510632, Guangdong, Peoples R China 9.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Gastroenterol & Hepatol, 58 Zhong Shan Rd 2, Guangzhou 510080, Guangdong, Peoples R China 10.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Liver Surg, 58 Zhong Shan Rd 2, Guangzhou 510080, Guangdong, Peoples R China
通讯作者单位	中国科学院自动化研究所; 中国科学院分子影像重点实验室
推荐引用方式 GB/T 7714	Chen, Shuling,Feng, Shiting,Wei, Jingwei,et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging[J]. EUROPEAN RADIOLOGY,2019,29(8):4177-4187.
APA	Chen, Shuling.,Feng, Shiting.,Wei, Jingwei.,Liu, Fei.,Li, Bin.,...&Kuang, Ming.(2019).Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging.EUROPEAN RADIOLOGY,29(8),4177-4187.
MLA	Chen, Shuling,et al."Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging".EUROPEAN RADIOLOGY 29.8(2019):4177-4187.